文献类型： 外文期刊
作者： Xu, Xiaoming 1 ; Yin, Zekun 1 ; Yan, Lifeng 1 ; Yi, Huiguang 2 ; Wang, Hua 1 ; Schmidt, Bertil 3 ; Liu, Weiguo 1 ;
作者机构： 1.Shandong Univ, Sch Software, Jinan, Peoples R China
2.Chinese Acad Agr Sci, Agr Genom Inst, Genome Anal Lab,Minist Agr, Guangdong Lab Lingnan Modern Agr,Shenzhen Branch, Shenzhen, Peoples R China
3.Johannes Gutenberg Univ Mainz, Inst Comp Sci, Mainz, Germany
期刊名称：BIOINFORMATICS （ 影响因子：5.8； 五年影响因子：8.3 ）
ISSN： 1367-4803
年卷期： 2023 年 39 卷 11 期
页码：
收录情况： SCI
摘要： We propose RabbitKSSD, a high-speed genome distance estimation tool. Specifically, we leverage load-balanced task partitioning, fast I/O, efficient intermediate result accesses, and high-performance data structures to improve overall efficiency. Our performance evaluation demonstrates that RabbitKSSD achieves speedups ranging from 5.7 x to 19.8 x over Kssd for the time-consuming sketch generation and distance computation on commonly used workstations. In addition, it significantly outperforms Mash, BinDash, and Dashing2. Moreover, RabbitKSSD can efficiently perform all-vs-all distance computation for all RefSeq complete bacterial genomes (455 GB in FASTA format) in just 2 min on a 64-core workstation.
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